d. dealing with the uncertainty- I am keen to try an Empirical
     Bayes approach to accounting for these different sources of
     uncertainty.  Some of you might be familiar with Adrian Raftery's
     (http://www.stat.washington.edu/raftery/Research/Whales/whales.html)
     (and others) work with the Int'l Whaling Commission for modeling
     Bowhead whale pop dynamics.  He uses a Bayes Empirical Bayes
     approach that the Int'l Whaling Commission now accepts as the
     "std" methodology.  There's some similarity with salmon
     harvest mgmt problems and what I'm thinking about trying.

     The Empirical Bayes approach is not really Bayesian per se,
     it just recognizes that the parameters are random, like the
     natural mortality rates in the ocean will vary from year to
     year.  We would formulate probability distributions for the
     parameters- the probability distributions would have 
     "hyperparameters".  E.g., the distribution of initial survival
     rates is Beta with parameters alpha and beta. Then use the 
     historical data, e.g., 10 years of Grays Harbor coho, to estimate
     the hyperparameters.  Next to make a forecast for the coming
     year could sample from the probability dist'ns for the parameters
     and run the model.  Could repeatedly do this to get a probability
     distribution for the outputs- draw histograms of predicted escapement
     for a stock, etc.

     * what's the prognosis on getting historical coho data for effort 
       and CWT recoveries into rectangular matrices?

 2.  Non-normal state-space models.     
      - the normality assumption can lead to some unreasonable predicted
      abundances and catches, negative ones, when abundance and effort
      are quite low
      - it might be better to use something like a Poisson dist'n for
       the catches, (like Ray Hilborn did in a 1990 CJFAS article on tuna 
       migration) and maybe abundances
      - this will require different techniques for estimating the historical
       parameters (Monte Carlo methods), but the simulation for pre-season
       planning might be simple 

 3.  Integrating multiple types of fisheries.
      - to deal with overlapping fisheries, say sport and troll, the catch
       equations can be modified, and the observation equ'n in the SSM
       modeled (haven't worked this out but don't think it'll be hard)
      - to deal with fisheries for which the effort has different temporal
       resolution will be trickier (e.g. monthly sport effort vs weekly
       troll effort)

 4.  Other modifications to SSM
     - more complex spatial framework for inside fisheries
     - chinook maturation schedule/component
     - links with or integration of ocean conditions
     - putting in switch for catch ceiling/quotas  
     - selective fisheries
     - incidental mortality
     - *other things? 

  5. Going into "production mode"
     - calculating historical parameter estimates for a wide range of
       stocks over several years
     - carrying out tests to determine what parameters are or are not
       stock specific